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Chapter 10: Counting Methods

- 10.1 Counting by Systematic Listing
- 10.2 Using the Fundamental Counting Principle
- 10.3 Using Permutations and Combinations
- 10.4 Using Pascal’s Triangle
- 10.5 Counting Problems Involving “Not” and “Or”

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Counting by Systematic Listing

- One-Part Tasks
- Product Tables for Two-Part Tasks
- Tree Diagrams for Multiple-Part Tasks
- Other Systematic Listing Methods

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One-Part Tasks

The results for simple, one-part tasks can often be listed easily. For the task of tossing a fair coin, the list is heads, tails, with two possible results. For the task of rolling a single fair die the list is 1, 2, 3, 4, 5, 6, with six possibilities.

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Example: Selecting a Club President

Consider a club N with four members:

N = {Mike, Adam, Ted, Helen} or in abbreviated form N = {M, A, T, H}

In how many ways can this group select a president?

Solution

The task is to select one of the four members as president. There are four possible results: M, A, T, and H.

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Example: Product Tables for Two-Part Tasks

Determine the number of two-digit numbers that can be written using the digits from the set {2, 4, 6}.

Solution

The task consists of two parts:

1. Choose a first digit

2. Choose a second digit

The results for a two-part task can be pictured in a product table, as shown on the next slide.

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Example: Product Tables for Two-Part Tasks

Solution(continued)

From the table we obtain the list of possible results: 22, 24, 26, 42, 44, 46, 62, 64, 66.

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Example: Possibilities for Rolling a Pair of Distinguishable Dice

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Example: Electing Club Officers

Find the number of ways club N (previous slide) can elect a president and secretary.

Solution

The task consists of two parts:

1. Choose a president

2. Choose a secretary

The product table is pictured on the next slide.

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Example: Product Tables for Two-Part Tasks

Solution(continued)

From the table we see that there are 12 possibilities.

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Example: Electing Club Officers

Find the number of ways club N (previous slide) can appoint a committee of two members.

Solution

Using the table on the previous slide, this time the order of the letters (people) in a pair makes no difference. So there are 6 possibilities: MA, MT, MH, AT, AH, TH.

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Tree Diagrams for Multiple-Part Tasks

A task that has more than two parts is not easy to analyze with a product table. Another helpful device is a tree diagram, as seen in the next example.

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Example: Building Numbers From a Set of Digits

Find the number of three digit numbers that can be written using the digits from the set {2, 4, 6} assuming repeated digits are not allowed.

Solution

Second

Third

First

4

6

6

4

246

264

426

462

624

642

2

4

6

2

6

6

2

6 possibilities

2

4

4

2

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Other Systematic Listing Methods

There are additional systematic ways to produce complete listings of possible results besides product tables and tree diagrams. One of these ways is shown in the next example.

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Example: Counting Triangles in a Figure

How many triangles (of any size) are in the figure below?

D

Solution

E

C

One systematic approach is to label the points as shown, begin with A, and proceed in alphabetical

F

A

B

order to write all 3-letter combinations (like ABC, ABD, …), then cross out ones that are not triangles. There are 12 different triangles.

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Using the Fundamental Counting Principle

- Uniformity and the Fundamental Counting Principle
- Factorials
- Arrangements of Objects

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Uniformity Criterion for Multiple-Part Tasks

A multiple-part task is said to satisfy the uniformity criterion if the number of choices for any particular part is the same no matter which choices were selected for the previous parts.

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Fundamental Counting Principle

When a task consists of k separate parts and satisfies the uniformity criterion, if the first part can be done in n1 ways, the second part can be done in n2 ways, and so on through the k th part, which can be done in nk ways, then the total number of ways to complete the task is given by the product

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Example: Two-Digit Numbers

How many two-digit numbers can be made from the set {0, 1, 2, 3, 4, 5}? (numbers can’t start with 0.)

Solution

There are 5(6) = 30 two-digit numbers.

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Example: Two-Digit Numbers with Restrictions

How many two-digit numbers that do not contain repeated digits can be made from the set {0, 1, 2, 3, 4, 5} ?

Solution

There are 5(5) = 25 two-digit numbers.

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Example: Two-Digit Numbers with Restrictions

How many ways can you select two letters followed by three digits for an ID?

Solution

There are 26(26)(10)(10)(10) = 676,000 IDs possible.

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Factorials

For any counting number n, the product of all counting numbers from n down through 1 is called n factorial, and is denoted n!.

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Factorial Formula

For any counting number n, the quantity n factorial is given by

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Definition of Zero Factorial

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Arrangements of Objects

When finding the total number of ways to arrange a given number of distinct objects, we can use a factorial.

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Arrangements of n Distinct Objects

The total number of different ways to arrange n distinct objects is n!.

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Example: Arranging Books

How many ways can you line up 6 different books on a shelf?

Solution

The number of ways to arrange 6 distinct objects is 6! = 720.

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Arrangements of n Objects Containing Look-Alikes

The number of distinguishable arrangements of n objects, where one or more subsets consist of look-alikes (say n1 are of one kind, n2 are of another kind, …, and nk are of yet another kind), is given by

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Example: Distinguishable Arrangements

Determine the number of distinguishable arrangements of the letters of the word INITIALLY.

Solution

9 letters total

3 I’s and 2 L’s

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Using Permutations and Combinations

- Permutations
- Combinations
- Guidelines on Which Method to Use

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Permutations

In the context of counting problems, arrangements are often called permutations; the number of permutations of n things taken r at a time is denoted nPr. Applying the fundamental counting principle to arrangements of this type gives

nPr= n(n – 1)(n – 2)…[n – (r – 1)].

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Factorial Formula for Permutations

The number of permutations, or arrangements, of n distinct things taken r at a time, where rn, can be calculated as

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Example: IDs

How many ways can you select two letters followed by three digits for an ID if repeats are not allowed?

Solution

There are two parts:

1. Determine the set of two letters.

2. Determine the set of three digits.

Part 1

Part 2

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Example: Building Numbers From a Set of Digits

How many four-digit numbers can be written using the numbers from the set {1, 3, 5, 7, 9} if repetitions are not allowed?

Solution

Repetitions are not allowed and order is important, so we use permutations:

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Combinations

In the context of counting problems, subsets, where order of elements makes no difference, are often called combinations; the number of combinations of n things taken r at a time is denoted nCr.

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Factorial Formula for Combinations

The number of combinations, or subsets, of n distinct things taken r at a time, where rn, can be calculated as

Note:

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Example: Finding the Number of Subsets

Find the number of different subsets of size 3 in the set {m, a, t, h, r, o, c, k, s}.

Solution

A subset of size 3 must have 3 distinct elements, so repetitions are not allowed. Order is not important.

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Example: Finding the Number of Subsets

A common form of poker involves hands (sets) of five cards each, dealt from a deck consisting of 52 different cards. How many different 5-card hands are possible?

Solution

Repetitions are not allowed and order is not important.

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Guidelines on Which Method to Use

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Pascal’s Triangle

The triangular array on the next slide represents “random walks” that begin at START and proceed downward according to the following rule. At each circle (branch point), a coin is tossed. If it lands heads, we go downward to the left. If it lands tails, we go downward to the right. At each point, left an right are equally likely. In each circle the number of different routes that could bring us to that point are recorded.

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Pascal’s Triangle

Another way to generate the same pattern of numbers is to begin with 1s down both diagonals and then fill in the interior entries by adding the two numbers just above the given position. The pattern is shown on the next slide. This unending “triangular array of numbers is called Pascal’s triangle.

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Combination Values in Pascal’s Triangle

The “triangle” possesses may properties. In counting applications, entry number r in row number n is equal to nCr – the number of combinations of n things taken r at a time. The next slide shows part of this correspondence.

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Example: Applying Pascal’s Triangle to Counting People

A group of seven people includes 3 women and 4 men. If five of these people are chosen at random, how many different samples of five people are possible?

Solution

Since this is really selecting 5 from a set of 7. We can read 7C5 from row 7 of Pascal’s triangle. The answer is 21

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Example: Applying Pascal’s Triangle to Counting People

Among the 21 possible samples of five people in the last example, how many of them would consist of exactly 2 women?

Solution

To select the women (2), we have 3C2 ways. To select the men (3), we have 4C3 ways. This gives a total of

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Example: Applying Pascal’s Triangle to Coin Tossing

If six fair coins are tossed, in how many different ways could exactly four heads be obtained?

Solution

There are various “ways” of obtaining exactly four heads because the four heads can occur on different subsets of coins. The answer is the number of size-four subsets of a size-six subset. This answer is from row 6 of Pascal’s triangle:

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Summary of Tossing Six Fair Coins

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Counting Problems Involving “Not” and “Or”

- Problems Involving “Not”
- Problems Involving “Or”

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Counting Problems Involving “Not” and “Or”

The counting techniques in this section, which can be thought of as indirect techniques, are based on some correspondences between set theory, logic, and arithmetic as shown on the next slide.

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Set Theory/Logic/Arithmetic Correspondences

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Problems Involving “Not”

Suppose U is the set of all possible results of some type. Let A be the set of all those results that satisfy a given condition. The figure below suggests that

U

A

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Complements Principle of Counting

The number of ways a certain condition can be satisfied is the total number of possible results minus the number of ways the condition would not be satisfied. Symbolically, if A is any set within the universal set U, then

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Example: Counting the Proper Subsets of a Set

For the set S = {c, a, l, u, t, o, r}, find the number of proper subsets.

Solution

A proper subset has less than seven elements. Subsets of many sizes would satisfy this condition. It is easier to consider the one subset that is not proper, namely S itself. S has a total of 27 = 128 subsets. From the complements principle, the number of proper subsets is 128 – 1 = 127.

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Example: Counting Coin Tossing

If five fair coins are tossed, in how many ways can at least one tail be obtained?

Solution

By the fundamental counting principle, there are 25 = 32 different results possible. Exactly one of these fails to satisfy “at least one tail.” So from the complement principle we have the answer: 32 – 1 = 31.

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Problems Involving “Or”

Another technique to count indirectly is to count the elements of a set by breaking that set into simpler component parts. If the cardinal number formula (Section 2.4) says to find the number of elements in S by adding the number in A to the number in B. We must then subtract the number in the intersection if A and B are not disjoint. If A and B are disjoint, the subtraction is not necessary.

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Additive Principle of Counting

The number of ways that one or the other of two conditions could be satisfied is the number of ways one of them could be satisfied plus the number of ways the other could be satisfied minus the number of ways they could both be satisfied together.

If A and B are any two sets, then

If A and B are disjoint, then

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Example: Counting Card Hands

How many five-card hands consist of all hearts or all black cards?

Solution

The sets all hearts and all black cards are disjoint. n(all hearts or all black cards)

= n(all hearts) + n(all black cards)

= 13C5 + 26C5

= 1,287 + 65,780 = 67,067

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Example: Counting Selections From a Diplomatic Delegation

A diplomatic delegation of 20 congressional members are categorized as to political party and gender. If one of the members is chosen randomly to be spokesperson for the group, in how many ways could that person be a Democrat or a man?

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Example: Counting Three-Digit Numbers with Conditions

How many three-digit counting numbers are multiples of 2 or multiples of 5?

Solution

There are 9(10)(5) = 450 three-digit multiples of 2. A multiple of 5 ends in a 0 or 5, so there are 9(10)(2) = 180 of those. A multiple of 2 and 5 must end in a 0. There are 9(10)(1) = 90 of those.

So we have 450 + 180 – 90 = 540 three-digit counting numbers that are multiples of 2 or 5.

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Example: Counting Card-Drawing Results

A single card is drawn from a standard 52-card deck. a) In how many ways could it be a club or a queen?

b) In how many ways could it be a red card or a face

card?

Solution

a) club + queen – queen of clubs = 13 + 4 – 1 = 16?

b) red card + face card – red face cards

= 26 + 12 – 6 = 32

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Chapter 11: Probability

- 11.1 Basic Concepts
- 11.2 Events Involving “Not” and “Or”
- 11.3 Conditional Probability; Events Involving “And”
- 11.4 Binomial Probability
- 11.5 Expected Value

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Basic Concepts

- Historical Background
- Probability
- The Law of Large Numbers
- Probability in Genetics
- Odds

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Historical Background

Much of the early work in probability concerned games and gambling. One of the first to apply probability to matters other than gambling was Pierre Simon de Laplace, who is often credited with being the “father” of probability theory. In the twentieth century a coherent mathematical theory of probability was developed through people such as Chebyshev, Markov, and Kolmogorov.

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Probability

The study of probability is concerned with random phenomena. Even though we cannot be certain whether a given result will occur, we often can obtain a good measure of its likelihood, or probability.

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Probability

In the study of probability, any observation, or measurement, of a random phenomenon is an experiment. The possible results of the experiment are called outcomes, and the set of all possible outcomes is called the sample space.

Usually we are interested in some particular collection of the possible outcomes. Any such subset of the sample space is called an event.

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Example: Tossing a Coin

If a single fair coin is tossed, find the probability that it will land heads up.

Solution

The sample space S = {h, t}, and the event whose probability we seek is E = {h}.

P(heads) = P(E) = 1/2.

Since no coin flipping was actually involved, the desired probability was obtained theoretically.

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Theoretical Probability Formula

If all outcomes in a sample space S are equally likely, and E is an event within that sample space, then the theoretical probability of the event E is given by

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Example: Flipping a Cup

A cup is flipped 100 times. It lands on its side 84 times, on its bottom 6 times, and on its top 10 times. Find the probability that it will land on its top.

Solution

From the experiment it appears that

P(top) = 10/100 = 1/10.

This is an example of experimental, or empirical probability.

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Empirical Probability Formula

If E is an event that may happen when an experiment is performed, then the empirical probability of event E is given by

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Example: Card Hands

There are 2,598,960 possible hands in poker. If there are 36 possible ways to have a straight flush, find the probability of being dealt a straight flush.

Solution

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Example: Gender of a Student

A school has 820 male students and 835 female students. If a student from the school is selected at random, what is the probability that the student would be a female?

Solution

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The Law of Large Numbers

As an experiment is repeated more and more times, the proportion of outcomes favorable to any particular event will tend to come closer and closer to the theoretical probability of that event.

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Probability in Genetics

Gregor Mendel, an Austrian monk used the idea of randomness to establish the study of genetics. To study the flower color of certain pea plants he found that: Pure red crossed with pure white produces red.

Mendel theorized that red is “dominant” (symbolized by R), while white is recessive (symbolized by r). The pure red parent carried only genes for red (R), and the pure white parent carried only genes for white (r).

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Probability in Genetics

Every offspring receives one gene from each parent which leads to the tables below. Every second generation is red because R is dominant.

1st to 2nd Generation

2nd to 3rd Generation

offspring

offspring

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Example: Probability of Flower Color

Referring to the 2nd to 3rd generation table (previous slide), determine the probability that a third generation will be

a) red b) white

Base the probability on the sample space of equally likely outcomes: S = {RR, Rr, rR, rr}.

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Example: Probability of Flower Color

Solution

a) Since red dominates white, any combination with R will be red. Three out of four have an R, so P(red) = 3/4.

b) Only one combination rr has no gene for red, so P(white) = 1/4.

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Odds

Odds compare the number of favorable outcomes to the number of unfavorable outcomes. Odds are commonly quoted in horse racing, lotteries, and most other gambling situations.

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Odds

If all outcomes in a sample space are equally likely, a of them are favorable to the event E, and the remaining b outcomes are unfavorable to E, then the oddsin favor of E are a to b, and the odds against E are b to a.

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Example: Odds

200 tickets were sold for a drawing to win a new television. If Matt purchased 10 of the tickets, what are the odds in favor of Matt’s winning the television?

Solution

Matt has 10 chances to win and 190 chances to lose. The odds in favor of winning are 10 to 190, or 1 to 19.

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Example: Converting Probability to Odds

Suppose the probability of rain today is .43. Give this information in terms of odds.

Solution

We can say that

43 out of 100 outcomes are favorable, so 100 – 43 = 57 are unfavorable. The odds in favor of rain are 43 to 57 and the odds against rain are 57 to 43.

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Example: Converting Odds to Probability

Your odds of completing College Algebra class are 16 to 9. What is the probability that you will complete the class?

Solution

There are 16 favorable outcomes and 9 unfavorable. This gives 25 possible outcomes. So

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Events Involving “Not” and “Or”

- Properties of Probability
- Events Involving “Not”
- Events Involving “Or”

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Properties of Probability

Let E be an event from the sample space S. That is, E is a subset of S. Then the following properties hold.

(The probability of an event is between 0 and 1, inclusive.)

(The probability of an impossible event is 0.)

(The probability of a certain event is 1.)

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Example: Rolling a Die

When a single fair die is rolled, find the probability of each event.

a) the number 3 is rolled

b) a number other than 3 is rolled

c) the number 7 is rolled

d) a number less than 7 is rolled

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Example: Rolling a Die

Solution

The outcome for the die has six possibilities: {1, 2, 3, 4, 5, 6}.

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Events Involving “Not”

The table on the next slide shows the correspondences that are the basis for the probability rules developed in this section. For example, the probability of an event not happening involves the complement and subtraction.

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Correspondences

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Probability of a Complement

The probability that an event E will not occur is equal to one minus the probability that it will occur.

E

S

So we have

and

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Example: Complement

When a single card is drawn from a standard 52-card deck, what is the probability that is will not be an ace?

Solution

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Events Involving “Or”

Probability of one event or another should involve the union and addition.

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Mutually Exclusive Events

Two events A and B are mutually exclusive events if they have no outcomes in common. (Mutually exclusive events cannot occur simultaneously.)

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Addition Rule of Probability (for A or B)

If A and B are any two events, then

If A and B are mutually exclusive, then

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Example: Probability Involving “Or”

When a single card is drawn from a standard 52-card deck, what is the probability that it will be a king or a diamond?

Solution

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Example: Probability Involving “Or”

If a single die is rolled, what is the probability of a 2 or odd?

Solution

These are mutually exclusive events.

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Chapter 11: Probability

- 11.1 Basic Concepts
- 11.2 Events Involving “Not” and “Or”
- 11.3 Conditional Probability; Events Involving “And”
- 11.4 Binomial Probability
- 11.5 Expected Value

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Conditional Probability; Events Involving “And”

- Conditional Probability
- Events Involving “And”

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Conditional Probability

Sometimes the probability of an event must be computed using the knowledge that some other event has happened (or is happening, or will happen – the timing is not important). This type of probability is called conditional probability.

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Conditional Probability

The probability of event B, computed on the assumption that event A has happened, is called the conditional probability of B, given A, and is denoted P(B | A).

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Example: Selecting From a Set of Numbers

From the sample space S = {2, 3, 4, 5, 6, 7, 8, 9}, a single number is to be selected randomly. Given the events

A: selected number is odd, and

B selected number is a multiple of 3.

find each probability.

a) P(B)

b) P(A and B)

c) P(B | A)

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Example: Selecting From a Set of Numbers

Solution

a) B = {3, 6, 9}, so P(B) = 3/8

b) P(A and B) = {3, 5, 7, 9} {3, 6, 9} = {3, 9}, so

P(A and B) = 2/8 = 1/4

c) The given condition A reduces the sample space

to {3, 5, 7, 9}, so P(B | A) = 2/4 = 1/2

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Conditional Probability Formula

The conditional probability of B, given A, and is given by

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Example: Probability in a Family

Given a family with two children, find the probability that both are boys, given that at least one is a boy.

Solution

Define S = {gg, gb, bg, bb}, A = {gb, bg, bb}, and B = {bb}.

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Independent Events

Two events A and B are called independent events if knowledge about the occurrence of one of them has no effect on the probability of the other one, that is, if

P(B | A) = P(B), or equivalently

P(A | B) = P(A).

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Example: Checking for Independence

A single card is to be drawn from a standard 52-card deck. Given the events

A: the selected card is an ace

B: the selected card is red

a) Find P(B).

b) Find P(B | A).

c) Determine whether events A and B are independent.

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Example: Checking for Independence

Solution

c. Because P(B | A) = P(B), events A and B are independent.

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Events Involving “And”

If we multiply both sides of the conditional probability formula by P(A), we obtain an expression for P(A and B). The calculation of P(A and B) is simpler when A and B are independent.

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Multiplication Rule of Probability

If A and B are any two events, then

If A and B are independent, then

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Example: Selecting From an Jar of Balls

Jeff draws balls from the jar below. He draws two balls without replacement. Find the probability that he draws a red ball and then a blue ball, in that order.

4 red

3 blue

2 yellow

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Example: Selecting From an Jar of Balls

Jeff draws balls from the jar below. He draws two balls, this time with replacement. Find the probability that he gets a red and then a blue ball, in that order.

4 red

3 blue

2 yellow

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Example: Selecting From an Jar of Balls

Solution

Because the ball is replaced, repetitions are allowed. In this case, event B2 is independent of R1.

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Binomial Probability

- Binomial Probability Distribution
- Binomial Probability Formula

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Binomial Probability Distribution

The spinner below is spun twice and we are interested in the number of times a 2 is obtained (assume each sector is equally likely).

Think of outcome 2 as a “success” and outcomes 1 and 3 as “failures.” The sample space is

2

1

3

S = {(1, 1), (1, 2), (1, 3), (2, 1), (2, 2),

(2, 3), (3, 1), (3, 2), (3, 3)}.

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Binomial Probability Distribution

When the outcomes of an experiment are divided into just two categories, success and failure, the associated probabilities are called “binomial.” Repeated trials of the experiment, where the probability of success remains constant throughout all repetitions, are also known as Bernoulli trials.

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Binomial Probability Distribution

If x denotes the number of 2s occurring on each pair of spins, then x is an example of a random variable. In S, the number of 2s is 0 in four cases, 1 in four cases, and 2 in one case. Because the table on the next slide includes all the possible values of x and their probabilities it is an example of a probability distribution. In this case, it is a binomial probability distribution.

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Probability Distribution for the Number of 2s in Two Spins

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Binomial Probability Formula

In general, let

n = the number of repeated trials,

p = the probability of success on any given trial,

q = 1 – p = the probability of failure on any given trial,

and x = the number of successes that occur.

Note that p remains fixed throughout all n trials. This means that all trials are independent. In general, x, successes can be assigned among n repeated trials in nCxdifferent ways.

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Binomial Probability Formula

When n independent repeated trials occur, where

p = probability of success and

q = probability of failure

with p and q (where q = 1 – p) remaining constant throughout all n trials, the probability of exactly x successes is given by

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Example: Coin Tossing

Find the probability of obtaining exactly three heads in five tosses of a fair coin.

Solution

This is a binomial experiment with n = 5, p = 1/2, q = 1/2, and x = 3.

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Example: Rolling a Die

Find the probability of obtaining exactly two 3’s in six rolls of a fair die.

Solution

This is a binomial experiment with n = 6, p = 1/6, q = 5/6, and x = 2.

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Example: Rolling a Die

Find the probability of obtaining less than two 3’s in six rolls of a fair die.

Solution

We have n = 6, p = 1/6, q = 5/6, and x < 2.

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Example: Baseball Hits

A baseball player has a well-established batting average of .250. In the next series he will bat 10 times. Find the probability that he will get more than two hits.

Solution

In this case n = 10, p = .250, q = .750, and x > 2.

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Expected Value

- Expected Value
- Games and Gambling
- Investments
- Business and Insurance

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Expected Value

Children in third grade were surveyed and told to pick the number of hours that they play electronic games each day. The probability distribution is given below.

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Expected Value

Compute a “weighted average” by multiplying each possible time value by its probability and then adding the products.

1.1 hours is the expected value (or the mathematical expectation) of the quantity of time spent playing electronic games.

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Expected Value

If a random variable x can have any of the values x1, x2 , x3 ,…, xn, and the corresponding probabilities of these values occurring are

P(x1), P(x2), P(x3), …, P(xn), then the expected value of xis given by

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Example: Finding Expected Value

Find the expected number of boys for a three-child family. Assume girls and boys are equally likely.

Solution

S = {ggg, ggb, gbg, bgg, gbb, bgb, bbg, bbb}

The probability distribution is on the right.

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Example: Finding Expected Value

Solution(continued)

The expected value is the sum of the third column:

So the expected number of boys is 1.5.

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Example: Finding Expected Winnings

A player pays $3 to play the following game: He rolls a die and receives $7 if he tosses a 6 and $1 for anything else. Find the player’s expected net winnings for the game.

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Example: Finding Expected Winnings

Solution

The information for the game is displayed below.

Expected value: E(x) = –$6/6 = –$1.00

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Games and Gambling

A game in which the expected net winnings are zero is called a fair game. A game with negative expected winnings is unfair against the player. A game with positive expected net winnings is unfair in favor of the player.

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Example: Finding the Cost for a Fair Game

What should the game in the previous example cost so that it is a fair game?

Solution

Because the cost of $3 resulted in a net loss of $1, we can conclude that the $3 cost was $1 too high. A fair cost to play the game would be $3 – $1 = $2.

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Investments

Expected value can be a useful tool for evaluating investment opportunities.

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Example: Expected Investment Profits

Mark is going to invest in the stock of one of the two companies below. Based on his research, a $6000 investment could give the following returns.

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Example: Expected Investment Profits

Find the expected profit (or loss) for each of the two stocks.

Solution

ABC: –$400(.2) + $800(.5) + $1300(.3) = $710

PDQ: $600(.8) + $1000(.2) = $680

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Business and Insurance

Expected value can be used to help make decisions in various areas of business, including insurance.

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Example: Expected Lumber Revenue

A lumber wholesaler is planning on purchasing a load of lumber. He calculates that the probabilities of reselling the load for $9500, $9000, or $8500 are .25, .60, and .15, respectfully. In order to ensure an expected profit of at least $2500, how much can he afford to pay for the load?

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Example: Expected Lumber Revenue

Solution

The expected revenue from sales can be found below.

Expected revenue: E(x) = $9050

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Example: Expected Lumber Revenue

Solution(continued)

profit = revenue – cost or cost = profit – revenue

To have an expected profit of $2500, he can pay up to $9050 – $2500 = $6550.

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